CHIKV 28dpi scRNA-seq
Ryan Sheridan
2024-12-04
Page 1
1C-D
UMAP projections show sample groups (left), cell type annotations (middle), and CHIKV+ cells (right).
The fraction of cells identified for each cell type is shown below.
1E
The number of differentially expressed genes identified for mock vs CHIKV is shown below for each cell type.
1F
Bargraphs showing select GSEA disease ontology terms for genes upregulated when comparing all mock vs CHIKV-infected macrophages. This shows the top terms sorted by adjusted p value, with “bone inflammation disease” added to the plot. GSEA terms were filtered to remove terms that include >400 genes.
Enrichment is shown for select top GSEA terms
Page 2
2B
The fraction of CHIKV+ cells identified for each cell type is shown below.
The fraction of cells identified as CHIKV+ is shown below for each macrophage subset from the CHIKV-infected samples. Unassigned cells are not shown.
Page 4
4A-B
UMAP projections show macrophage subsets.
4C
UMAP projections show CHIKV+ cells (left) and CHIKV-high clusters (middle, right) for CHIKV-infected samples.
4D
Heatmaps show expression of the top 20 genes upregulated in CHIKV- vs mock-infected samples for each macrophage subset. Genes are arranged by the number of subsets upregulating the gene and the maximum adjusted p-value for the replicates. Black diamonds mark the top 20 upregulated genes for each subset, grey diamonds mark other subsets where each gene is upregulated.
This is an alternate version showing the top 30 genes sorted by the maximum fold change in expression for the replicates. This modified version includes Il1b and Tnf.
Page 5
5A
Expression of select genes upregulated in CHIKV- vs mock-infected samples is shown below for macrophage subsets.
5D
Heatmaps show expression of the top 10 marker genes for each macrophage subset. Genes are sorted by minimum fold change for the replicates.
Page 6
6A
Ifng expression is shown below for each cell type for CHIKV-infected samples. CD4 Teffs are plotted as a separate group.
6B
UMAP projections show T cell subsets.
6C
Heatmaps show expression of the top 15 genes upregulated in CHIKV- vs mock-infected samples for each T cell subset. Genes are arranged by the number of cell types upregulating the gene and the maximum adjusted p-value for the replicates. Black diamonds mark the top 15 upregulated genes for each subset, grey diamonds mark other subsets where each gene is upregulated.
6D
Ifng expression is shown below for each T cell subset.
Page 7
7A
T cell marker genes are shown below for mock- and CHIKV-infected samples.
Page 11
11A
Heatmaps show expression of the top 10 marker genes for each macrophage subset. Genes are sorted by minimum fold change for the replicates.
Session info
## R version 4.3.1 (2023-06-16)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 22.04.5 LTS
##
## Matrix products: default
## BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
## LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so; LAPACK version 3.10.0
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
## [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
## [7] LC_PAPER=en_US.UTF-8 LC_NAME=en_US.UTF-8
## [9] LC_ADDRESS=en_US.UTF-8 LC_TELEPHONE=en_US.UTF-8
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=en_US.UTF-8
##
## time zone: America/Denver
## tzcode source: system (glibc)
##
## attached base packages:
## [1] stats4 stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] qs_0.25.7 edgeR_4.0.12
## [3] limma_3.58.1 metap_1.9
## [5] harmony_1.2.0 gprofiler2_0.2.2
## [7] presto_1.0.0 data.table_1.14.10
## [9] Rcpp_1.0.11 M3Drop_1.28.0
## [11] numDeriv_2016.8-1.1 DoubletFinder_2.0.3
## [13] scuttle_1.12.0 SingleCellExperiment_1.24.0
## [15] SummarizedExperiment_1.32.0 GenomicRanges_1.54.1
## [17] GenomeInfoDb_1.38.5 MatrixGenerics_1.14.0
## [19] matrixStats_1.2.0 clustifyrdata_1.1.0
## [21] clustifyr_1.14.0 SeuratObject_4.1.4
## [23] Seurat_4.4.0 ggupset_0.3.0
## [25] patchwork_1.1.3 ggrepel_0.9.4
## [27] scales_1.3.0 djvdj_0.1.0
## [29] colorblindr_0.1.0 colorspace_2.1-0
## [31] ggtrace_0.2.0.9000 furrr_0.3.1
## [33] future_1.33.1 xlsx_0.6.5
## [35] knitr_1.45 cowplot_1.1.2
## [37] here_1.0.1 broom_1.0.5
## [39] lubridate_1.9.3 forcats_1.0.0
## [41] stringr_1.5.1 dplyr_1.1.4
## [43] purrr_1.0.2 readr_2.1.4
## [45] tidyr_1.3.0 tibble_3.2.1
## [47] ggplot2_3.4.4 tidyverse_2.0.0
## [49] org.Hs.eg.db_3.18.0 org.Mm.eg.db_3.18.0
## [51] AnnotationDbi_1.64.1 IRanges_2.36.0
## [53] S4Vectors_0.40.2 Biobase_2.62.0
## [55] BiocGenerics_0.48.1 biomaRt_2.58.0
## [57] DOSE_3.28.2 msigdbr_7.5.1
## [59] enrichplot_1.22.0 clusterProfiler_4.10.0
##
## loaded via a namespace (and not attached):
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## [3] nnet_7.3-19 goftest_1.2-3
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## [7] rstan_2.32.3 vctrs_0.6.5
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## [11] digest_0.6.33 png_0.1-8
## [13] deldir_2.0-2 parallelly_1.36.0
## [15] MASS_7.3-60 reshape2_1.4.4
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